{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:30:02Z","timestamp":1783611002671,"version":"3.55.0"},"reference-count":60,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["NRF-2023R1A2C2005548"],"award-info":[{"award-number":["NRF-2023R1A2C2005548"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["RS-2022-00155763"],"award-info":[{"award-number":["RS-2022-00155763"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007694","name":"Korea Agency for Infrastructure Technology Advancement (KAIA)","doi-asserted-by":"publisher","award":["NRF-2023R1A2C2005548"],"award-info":[{"award-number":["NRF-2023R1A2C2005548"]}],"id":[{"id":"10.13039\/501100007694","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007694","name":"Korea Agency for Infrastructure Technology Advancement (KAIA)","doi-asserted-by":"publisher","award":["RS-2022-00155763"],"award-info":[{"award-number":["RS-2022-00155763"]}],"id":[{"id":"10.13039\/501100007694","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image super-resolution (SR) is a significant technique in image processing as it enhances the spatial resolution of images, enabling various downstream applications. Based on recent achievements in SR studies in computer vision, deep-learning-based SR methods have been widely investigated for remote sensing images. In this study, we proposed a two-stage approach called bicubic-downsampled low-resolution (LR) image-guided generative adversarial network (BLG-GAN) for remote sensing image super-resolution. The proposed BLG-GAN method divides the image super-resolution procedure into two stages: LR image transfer and super-resolution. In the LR image transfer stage, real-world LR images are restored to less blurry and noisy bicubic-like LR images using guidance from synthetic LR images obtained through bicubic downsampling. Subsequently, the generated bicubic-like LR images are used as inputs to the SR network, which learns the mapping between the bicubic-like LR image and the corresponding high-resolution (HR) image. By approaching the SR problem as finding optimal solutions for subproblems, the BLG-GAN achieves superior results compared to state-of-the-art models, even with a smaller overall capacity of the SR network. As the BLG-GAN utilizes a synthetic LR image as a bridge between real-world LR and HR images, the proposed method shows improved image quality compared to the SR models trained to learn the direct mapping from a real-world LR image to an HR image. Experimental results on HR satellite image datasets demonstrate the effectiveness of the proposed method in improving perceptual quality and preserving image fidelity.<\/jats:p>","DOI":"10.3390\/rs15133309","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T01:15:47Z","timestamp":1688001347000},"page":"3309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Enhancing Remote Sensing Image Super-Resolution Guided by Bicubic-Downsampled Low-Resolution Image"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1266-5566","authenticated-orcid":false,"given":"Minkyung","family":"Chung","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minyoung","family":"Jung","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongil","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1023\/A:1026501619075","article-title":"Learning Low-Level Vision","volume":"40","author":"Freeman","year":"2000","journal-title":"Int. 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